采用PSR和客观相似性的高置信度跟踪

宋华军; 于玮; 王芮

doi:10.3788/OPE.20182612.3067

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采用PSR和客观相似性的高置信度跟踪

信息科学 | 更新时间：2020-07-05

- 采用PSR和客观相似性的高置信度跟踪
- High-confidence correlation tracking algorithm based on PSR and objective similarity
- 光学精密工程 2018年26卷第12期页码：3067-3078
- 作者机构：
  
  中国石油大学(华东) 电子信息工程系, 山东青岛 266580
- 作者简介：
  
  [ "宋华军(1978-), 男, 山东威海人, 副教授, 2006年毕业于中国科学院长春光学精密机械与物理研究所, 获得博士学位, 主要从事模式识别、目标跟踪方面的研究。E-mail:huajun.song@upc.edu.cn" ]
  [ "于玮(1996-), 男, 山东威海人, 硕士研究生, 2017年于中国石油大学(华东)获得学士学位, 主要从事目标跟踪方面的研究。E-mail:yw19960216@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61602517);中央高校基本科研业务费专项资金资助项目(18CX02109A)
- DOI：10.3788/OPE.20182612.3067
  中图分类号： TP391
- 收稿日期：2018-04-19，
  
  录用日期：2018-5-23，
  
  纸质出版日期：2018-12-25
- 稿件说明：
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宋华军, 于玮, 王芮. 采用PSR和客观相似性的高置信度跟踪[J]. 光学精密工程, 2018,26(12):3067-3078.

Hua-jun SONG, WEI YU, Rui WANG. High-confidence correlation tracking algorithm based on PSR and objective similarity[J]. Optics and precision engineering, 2018, 26(12): 3067-3078.
宋华军, 于玮, 王芮. 采用PSR和客观相似性的高置信度跟踪[J]. 光学精密工程, 2018,26(12):3067-3078. DOI： 10.3788/OPE.20182612.3067.

Hua-jun SONG, WEI YU, Rui WANG. High-confidence correlation tracking algorithm based on PSR and objective similarity[J]. Optics and precision engineering, 2018, 26(12): 3067-3078. DOI： 10.3788/OPE.20182612.3067.

摘要

针对相关滤波类跟踪算法难以解决的过度形变和目标被遮挡问题，提出了一种融合改进均方峰值旁瓣和客观相似性度量的高置信度跟踪算法-HCF。基于核相关滤波跟踪算法，结合传统相关运算的峰值旁瓣比与感知哈希算法客观度量所跟目标，对遮挡和形变等复杂情况进行高置信度判断，进而自适应的选择模型更新率，克服模型漂移问题；另外，利用尺度池算法解决跟踪中的尺度估计问题，进一步提高了算法的稳健性。通过OTB-2015数据集测试表明：提出的HCF算法能精准判别出由于遮挡形变等情况导致的无效跟踪，相比于当前主流的鲁棒性跟踪算法，具有更优秀的性能和表现。本文的创新工作为跟踪领域中的目标准确度判别问题提供了新的思路。

Abstract

To prevent over-deformation and to solve occlusion problems that are difficult to solve for correlated filtering tracking algorithms in the tracking field

an improved multiscale target tracking algorithm based on PSR and objective similarity was proposed in this paper. The proposed method combined a traditional correlation operation

peak side lobe ratio

with a perceptual hashing algorithm to tackle problems such as target occlusion

over deformation

and other complex scene judgments. Experimental results using the OTB-2015 demonstrate proposed algorithm's reliability and integrity of the target trajectory. The accuracy and robustness of our algorithm is better than that of Kernelized Correlation Filter (KCF) tracking algorithms. This paper presents a novel idea for occlusion detection in the target tracking field.

关键词

Keywords

references

CHEN Z, HONG Z, TAO D. An.experimental survey on correlation filter-based tracking[J]. Computer Science, 2015, 53(6025):68-83.

ZHAO G, SHEN Y, WANG J, et al.. Adaptive feature fusion object tracking based on circulant structure with kernel[J]. Acta Optica Sinica, 2017, 37(8):0815001.

马俊凯, 罗海波, 常铮, 等.基于可变形模型的目标跟踪算法[J].红外与激光工程, 2017, 46(9):292-300.

MA J K, LUO H B, CHANG ZH, et al.. Visual tracking algorithm based on deformable parts model[J]. Infrared and laser engineering, 2017, 46(9):292-300.(in Chinese)

张雷, 王延杰, 孙宏海, 等.采用核相关滤波器的自适应尺度目标跟踪[J].光学精密工程, 2016, 24(2):448-459.

ZHANG L, WANG Y J, SUN H H, et al.. Adaptive scale target tracking using nuclear correlation filter[J]. Opt. Precision Eng., 2016, 24(2):448-459.(in Chinese)

BOLME D, BEVERIDGE J R, DRAPER B A, et al.. Visual object tracking using adaptive correlation filters[J]. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010:2544-2550.

HENRIQUES J F, CASEIRO R, MARTINS P, et al.. Exploiting the circulant structure of tracking-by-detection with kernels[J]. Computer Vision-ECCV 2012, 2012:702-715.

DANELLJAN M, KHAN F S, FELSBERG M, et al.. Adaptive color attributes for real-time visual tracking[J]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014:1090-1097.

HENRIQUES J F, CASEIRO R, MARTINS P, et al.. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(3):583-596.

DANELLJAN M, H GER G, SHAHBAZ KHAN F, et al.. Accurate scale estimation for robust visual tracking[J]. Proceedings of the British Machine Vision Conference, 2014.

ZHANG K, ZHANG L, LIU Q, et al.. Fast visual tracking via dense spatio-temporal context learning[J]. Computer Vision-ECCV, 2014:127-141.

BERTINETTO L, VALMADRE J, GOLODETZ S, et al.. Staple:complementary learners for real-time tracking[J]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

GALOOGAHI H K, FAGG A, LUCEY S. Learning background-aware correlation filters for visual tracking[J]. 2017 IEEE International Conference on Computer Vision (ICCV), 2017.

MA C, YANG X, ZHANG C, et al.. Long-term correlation tracking[C]. Computer Vision and Pattern Recognition . IEEE , 2015: 5388-5396. https://www.researchgate.net/publication/304555000_Long-term_correlation_tracking

KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J]. IEEE Trans Pattern Anal Mach Intell, 2012, 34(7):1409-1422.

罗海波, 许凌云, 惠斌, 等.基于深度学习的目标跟踪方法研究现状与展望[J].红外与激光工程, 2017, 46(5):6-12.

LUO H B, XU L Y, HUI BIN, Status and prospect of target tracking based on deep learning[J]. Infrared and laser engineering, 2017, 46(5):6-12.(in Chinese)

WU Y, LIM J, YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(9):1834-1848.

闫辉, 许廷发, 吴青青, 等.多特征融合匹配的多目标跟踪[J].中国光学, 2013, 6(2):163-170.

YAN HUI, XU TF, WU QQ, et al.. Multi target tracking with multi feature fusion and matching[J]. China Optics, 2013, 6(2):163-170.(in Chinese)

颜佳, 吴敏渊, 陈淑珍, 等.应用Mean Shift和分块的抗遮挡跟踪[J].光学精密工程, 2010, 18(6):1413-1419.

YAN J, WU MY, CHEN SZ, et al.. Using Mean Shift and blocking anti occlusion tracking[J]. Opt. Precision Eng., 2010, 18(6):1413-1419. (in Chinese)

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition . IEEE Computer Society , 2005: 886-893. http://cn.bing.com/academic/profile?id=0796294dc798795c12f84f5b2edabaef&encoded=0&v=paper_preview&mkt=zh-cn

WENG L, PRENEEL B. Attacking some perceptual image hash algorithms[C]. IEEE International Conference on Multimedia and Expo . IEEE , 2007: 879-882. https://www.researchgate.net/publication/224718724_Attacking_Some_Perceptual_Image_Hash_Algorithms

DANELLJAN M, BHAT G, KHAN F S, et al.. ECO:Efficient convolution operators for tracking[J]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

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